Online research in philosophy

In two H215O PET scan experiments, we investigated the cerebral correlates of explicit and implicit knowledge in a serial reaction time (SRT) task. To do so, we used a novel application of the Process Dissociation Procedure, a behavioral paradigm that makes it possible to separately assess conscious and unconscious contributions to performance during a subsequent sequence generation task. To manipulate the extent to which the repeating sequential pattern was learned explicitly, we varied the pace of the choice reaction time task—a variable that is known to have differential effects on the extent to which sensitivity to sequence structure involves implicit or explicit knowledge. Results showed that activity in the striatum subtends the implicit component of performance during recollection of a learned sequence, whereas the anterior cingulate/mesial prefrontal cortex (ACC/MPFC) supports the explicit component. Most importantly, we found that the ACC/MPFC exerts control on the activity of the striatum during retrieval of the sequence after explicit learning, whereas the activity of these regions is uncoupled when learning had been essentially implicit. These data suggest that implicit learning processes can be successfully controlled by conscious knowledge when learning is essentially explicit. They also supply further evidence for a partial dissociation between the neural substrates supporting conscious and nonconscious components of performance during recollection of a learned sequence.

The ability to process events in their temporal and sequential context is a fundamental skill made mandatory by constant interaction with a dynamic environment. Sequence learning studies have demonstrated that subjects exhibit detailed — and often implicit — sensitivity to the sequential structure of streams of stimuli. Current connectionist models of performance in the so-called Serial Reaction Time Task (SRT), however, fail to capture the fact that sequence learning can be based not only on sensitivity to the sequential associations between successive stimuli, but also on sensitivity to the associations between successive responses, and on the predictive relationships that exist between these sequences of responses and their effects in the environment. In this paper, we offer an initial exploration of an alternative architecture for sequence learning, based on the principles of Forward Models.

Comparing the relative sensitivity of direct and indirect measures of learning is proposed as the best way to provide evidence for unconscious learning when both conceptual and operative definitions of awareness are lacking. This approach was first proposed by Reingold & Merikle (1988) in the context of subliminal perception. In this paper, we apply it to a choice reaction time task in which the material is generated based on a probabilistic finite-state grammar (Cleeremans, 1993). We show (1) that participants progressively learn about the statistical structure of the stimulus material over training with the choice reaction time task, and (2) that they can use some of this knowledge to predict the location of the next stimulus in a subsequent “generation” task. However, detailed partial correlational analyses of the correspondence between performance during the reaction time task and the statistical structure of the training material showed that large effects remained even when controlling for explicit knowledge as assessed by the generation task. Hence we conclude (1) that at least some of the knowledge expressed through reaction time performance can not be characterized as conscious, and (2) that even when associations are found at a global level of analysis, dissociations can still be obtained when more detailed analyses are conducted. Finally, we also show that participants are limited in the depth of the contingencies they can learn about, and that these limitations are shared by the Simple Recurrent Network model of Cleeremans & McClelland (1991).

What do people learn when they do not know that they are learning? Until recently, all of the work in the area of implicit learning focused on empirical questions and methods. In this book, Axel Cleeremans explores unintentional learning from an information-processing perspective. He introduces a theoretical framework that unifies existing data and models on implicit learning, along with a detailed computational model of human performance in sequence-learning situations.

Implicit learning – broadly construed as learning without awareness – is a complex, multifaceted phenomenon that defies easy definition. Frensch (1998) listed as many as eleven definitions in an overview, a diversity that is undoubtedly symptomatic of the conceptual and methodological challenges that continue to pervade the field forty years after the term first appeared in the literature (Reber, 1967). According to Berry and Dienes (1993), learning is implicit when an individual acquires new information without intending to do so and in such a way that the resulting knowledge is difficult to express. In this, implicit learning thus contrasts strongly with explicit learning (e.g., as when learning how to solve a problem or learning a concept), which is typically hypothesisdriven and fully conscious. Implicit learning is the process through which one becomes sensitive to certain regularities in the environment: (1) without trying to learn regularities, (2) without knowing that one is learning regularities, and (3) in such a way that the resulting knowledge is unconscious.

How do we find out whether someone is conscious of some information or not? A simple answer is “We just ask them”! However, things are not so simple. Here, we review recent developments in the use of subjective and objective methods in implicit learning research and discuss the highly complex methodological problems that their use raises in the domain.

Running head: Implicit sequence learning ABSTRACT Can we learn without awareness? Although this issue has been extensively explored through studies of implicit learning, there is currently no agreement about the extent to which knowledge can be acquired and projected onto performance in an unconscious way. The controversy, like that surrounding implicit memory, seems to be at least in part attributable to unquestioned acceptance of the unrealistic assumption that tasks are process-pure, that is, that a given task exclusively involves either implicit or explicit knowledge.

Sometimes we work out by ourselves how to do something. But often we rely upon the help, advice or example of others. To this extent learning how resembles learning that: sometimes you can see the truth for yourself, but sometimes you need to phone a friend. Do the similarities end there? When we are tempted to think that knowing how differs significantly from knowing that, it is often because knowing how seems to be transmitted, acquired, taught and learned in distinctive ways. Practical knowledge can’t always be obtained from books or lectures, it often requires hands-on experience, those who know how can’t always teach, and sometimes those who can’t do can nevertheless teach.

Studies of implicit learning have shown that individuals exposed to a rule-governed environment often learn to exploit 'rules' which describe the structural relationship between environmental events. While some authors have interpreted such demonstrations as evidence for functionally separate implicit learning systems, others have argued that the observed changes in performance result from explicit knowledge which has been inadequately assessed. In this paper we illustrate this issue by considering one commonly used implicit learning task, the Serial reaction time task, and outline what we see as an important problem associated with each of the commonly used methods used to assess explicit knowledge. This is that each measure requires a form of response which is dependent on the subjects having some knowledge of the serial-order of the sequence. We argue that such methods, or more specifically their analyses, seriously underestimate other sources of knowledge, which may be available to subjects during their performance of the SRT task. In support of this argument we demonstrate that subjects' serial-order knowledge can, in principle, be independent of subjects' knowledge of the statistical structure of the sequence, and we propose an alternative method for analysing performance on the Generate task which avoids this problem.